Cameras have become common in mobile devices, surveillance sensors, and law enforcement vehicles. Due to their mobility, such cameras can record images of individuals in a variety of unconstrained conditions. That is, in contrast to a staged mug shot, faces of individuals recorded under unconstrained conditions can vary greatly due to changes in lighting (e.g., natural and artificial), attributes of the individual's face (e.g., age, facial hair, glasses), viewing angle (e.g., pitch and yaw), occlusions (e.g., signs, trees, etc.), and the like. For example, a wrongdoer may perform an illegal act at a crowded event. Around a time of the act, bystanders may capture images of the wrongdoer while recording the event using their mobile cameras. Additionally, security cameras monitoring the event may capture images of the wrongdoer from different (e.g., elevated) perspectives. Coincidentally, the images of the wrongdoer may have been captured by a number of cameras having different perspectives and occlusions. The recordings may be accessed by law enforcement authorities from operators of the cameras, social networking websites, and media outlets. However, attempting to identify the wrongdoer from the various recordings can require sifting through an enormous amount of image data.